A Hybrid Feature Selection Approach for Microarray Gene Expression Data

  • Feng Tan
  • Xuezheng Fu
  • Hao Wang
  • Yanqing Zhang
  • Anu Bourgeois
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)


Due to the huge number of genes and comparatively small number of samples from microarray gene expression data, accurate classification of diseases becomes challenging. Feature selection techniques can improve the classification accuracy by removing irrelevant and redundant genes. However, the performance of different feature selection algorithms based on different theoretic arguments varies even when they are applied to the same data set. In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm. The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.


Genetic Algorithm Feature Selection Feature Subset Feature Selection Method Feature Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Feng Tan
    • 1
  • Xuezheng Fu
    • 1
  • Hao Wang
    • 1
  • Yanqing Zhang
    • 1
  • Anu Bourgeois
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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